remote sensing Article Mangrove and Saltmarsh Distribution Mapping and Land Cover Change Assessment for South-Eastern Australia from 1991 to 2015 Alejandro Navarro 1,*, Mary Young 1 , Peter I. Macreadie 2 , Emily Nicholson 2 and Daniel Ierodiaconou 1 1 School of Life & Environmental Sciences, Faculty of Science, Engineering and Built Environment, Deakin University, Warrnambool, VIC 3280, Australia; [email protected] (M.Y.); [email protected] (D.I.) 2 School of Life & Environmental Sciences, Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, VIC 3125, Australia; [email protected] (P.I.M.); [email protected] (E.N.) * Correspondence: [email protected] Abstract: Coastal wetland ecosystems, such as saltmarsh and mangroves, provide a wide range of important ecological and socio-economic services. A good understanding of the spatial and temporal distribution of these ecosystems is critical to maximising the benefits from restoration and conservation projects. We mapped mangrove and saltmarsh ecosystem transitions from 1991 to 2015 in south-eastern Australia, using remotely sensed Landsat data and a Random Forest classification. Our classification results were improved by the addition of two physical variables (Shuttle Radar Topographic Mission (SRTM), and Distance to Water). We also provide evidence that the addition Citation: Navarro, A.; Young, M.; of post-classification, spatial and temporal, filters improve overall accuracy of coastal wetlands Macreadie, P.I.; Nicholson, E.; detection by up to 16%. Mangrove and saltmarsh maps produced in this study had an overall User Ierodiaconou, D. Mangrove and Accuracy of 0.82–0.95 and 0.81–0.87 and an overall Producer Accuracy of 0.71–0.88 and 0.24–0.87 Saltmarsh Distribution Mapping and for mangrove and saltmarsh, respectively. We found that mangrove ecosystems in south-eastern Land Cover Change Assessment for Australia have lost an area of 1148 ha (7.6%), whilst saltmarsh experienced an overall increase in South-Eastern Australia from 1991 to coverage of 4157 ha (20.3%) over this 24-year period. The maps developed in this study allow local 2015. Remote Sens. 2021, 13, 1450. managers to quantify persistence, gains, and losses of coastal wetlands in south-eastern Australia. https://doi.org/10.3390/rs13081450 Keywords: mangrove; saltmarsh; distribution; south-eastern Australia; Landsat; land-cover change; Academic Editors: Daniel Gann and random forest Jennifer Richards Received: 22 February 2021 Accepted: 4 April 2021 1. Introduction Published: 8 April 2021 Coastal wetland ecosystems (mangrove, saltmarsh, and seagrasses) provide a wide Publisher’s Note: MDPI stays neutral range of important ecological and socio-economic services to coastal areas [1], including with regard to jurisdictional claims in provisioning, coastal protection, recreational and aesthetic uses, and climate change mit- published maps and institutional affil- igation through soil formation and carbon sequestration [2–5]. However, the extent of iations. coastal wetlands has significantly decreased worldwide as a result of anthropogenic im- pacts, particularly near populated areas [6,7]. Coastal wetland ecosystems are susceptible to a range of natural and anthropogenic-related threats, including climate change, and the associated impacts of sea level rise, storms, tidal surges, and changes in precipitation and temperature [8–10], coastal development, and conversion to agriculture, such as stock Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. grazing [11]. This article is an open access article In Australia, mangroves and saltmarshes occur throughout coastal regions, covering 2 distributed under the terms and an area of approximately 25,000 km [12,13]. Although they are protected under the conditions of the Creative Commons Environment Protection and Biodiversity Conservation Act 1999 [14], they have seen Attribution (CC BY) license (https:// extensive loss since European colonisation. Large losses of coastal wetland ecosystems creativecommons.org/licenses/by/ have been documented over the past 50 years due to increasing agriculture and urbanisation 4.0/). near estuarine fringes [15] or climate change-associated extreme weather events [8]. In Remote Sens. 2021, 13, 1450. https://doi.org/10.3390/rs13081450 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 1450 2 of 22 temperate regions of south-eastern Australia, around 20 percent of Australia’s mangroves and saltmarshes have been lost since colonisation [16], with some estuaries losing up to 80 percent [15]. As a consequence, coastal wetland ecosystems have been a focus of many conservation and restoration projects in temperate and sub-tropical regions of Australia [17–19]. The success of such projects requires a good understanding of spatial and temporal ecosystem distribution to inform better management strategies and to ensure effort is directed towards appropriate areas [20–22]. Comprehensive, statewide mapping and inventory studies of coastal wetlands have been completed over the past two decades along the south-eastern coast of Australia, using a mixture of airborne and on-ground surveys (Victoria [23] and New South Wales [19,24]). Although some historical mapping has been undertaken with the use of aerial photogra- phy [19,25] and archival maps [23] in specific study areas, the majority of these statewide maps were made for single years. This is a result of the methodologies used to obtain these maps (aerial and on-ground surveys), which are expensive and not widely available, often prohibiting the collection of frequent repeat surveys [26,27]. As a result, there is a lack of temporally consistent data at an appropriate temporal and spatial resolution for ecosystem monitoring and management. Remotely sensed satellite data provide a fast, cost-effective, and efficient method to monitor the distribution of coastal wetlands ecosystems [27–30]. The Landsat satellites (Thematic Mapper, TM; Enhanced TM+, ETM+; and Operational Land Imager, OLI) have been the most widely used sensors for mapping coastal wetlands distribution [28,31–34], as they provide access to a high temporal frequency (every 16 days), medium resolution (~30 m), long-term dataset over the past 35+ years [35]. This long temporal coverage allows investigation of transitional patterns of ecosystems and land uses across broad spatial scales [36–39]. While multiple mangrove maps have been developed at global scales [31,33], consistent and repeatable time-series are lacking, particularly those trained for local use mapping and application. Recent development of machine learning (ML) methods provides new opportunities for accurately mapping coastal wetlands ecosystems [34]. Traditionally, coastal wetland mapping using satellite data has been performed using individual vegetation indices [34]. Numerous classical vegetation indices (i.e., Normalised Difference Vegetation Index, En- hanced Vegetation Index, and Normalised Difference Water Index) and novel vegetation indices (i.e., Combined Mangrove Recognition Index and Modular Mangrove Recognition Index) have been tested for coastal wetlands detection [40,41]. These indices are generally good at differentiating flooded vegetated areas from adjacent water sources or non-flooded vegetation [40]. However, they often struggle to differentiate between specific flooded veg- etated types, such as mangroves, saltmarshes, and freshwater wetlands [42]. On the other hand, machine learning algorithms such as random forest, artificial neural networks, or support vector regression have proven to be more successful in mapping coastal wetlands distribution than single indices [40,42,43]. These models generally use a list of individual multispectral bands and vegetation indices [40,44]. However, although important for forest detection [45,46], physical variables such as Shuttle Radar Topographic Mission (SRTM) elevation and Distance to Water (DistW) have rarely been used for the detection of coastal wetlands [42]. As their name indicates, coastal wetlands are known for occurring across the land-sea interface [47]. Therefore, we hypothesised that the addition of a DistW layer would improve coastal wetland detection with satellite data. Additionally, these ecosys- tems have very low growth rates in south-eastern Australia due to the colder climate [48], and therefore will benefit from the use of SRTM elevation as a variable, despite it being collected for a single year (2000; [49]). In this study, we present triennial mangrove and saltmarsh distribution maps at 30 m resolution from 1991 to 2015 across the south-eastern coast of Australia. We use a combination of cloud-based (Google Earth Engine) and local computing to map coastal areas by applying a Random Forest (RF) model to Landsat time series data. We also propose the use of two physical variables (SRTM and Distance to Water) and a set of post- Remote Sens. 2021, 13, 1450 3 of 22 classification filters to improve mangrove and saltmarsh modelling. Finally, we perform a land-use change analysis to assess the gains and losses of mangrove and saltmarsh extent over the 24-year period. The map outputs from this study provide a long-term time series of the dynamics of these important coastal ecosystems, which can help with management by quantifying ecosystem losses or gains and persistence, along with impacts on their associated ecosystem services. 2. Materials and Methods 2.1. Study Area We focused our research on the south-eastern